Optimization of grinding processes using multi-criteria decision making methods in intuitionistic fuzzy environment

• Published in: Opsearch Vol. 61; no. 2; pp. 709 - 740
• Main Authors: Chowdhury, Samriddhya Ray, Chatterjee, Srinjoy, Chakraborty, Shankar
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.06.2024
• Subjects: Application Article; Business and Management; Management; Mathematics; Operations Research/Decision Theory; Intuitionistic fuzzy sets; Rank; Multi-criteria decision making; Grinding; Optimization
• DOI: 10.1007/s12597-024-00741-9
• ISSN: 0030-3887

To achieve better surface quality and close dimensional tolerance, finishing operations, like surface and cylindrical grinding are widely employed in many of the manufacturing industries. For determining the optimal values of various grinding parameters, like wheel speed, feed rate, depth of cut, width of cut, wheel material etc., multi-criteria decision making (MCDM) methods have already proven to be an effective way to simultaneously deal with multiple input parameters, affecting attainment of the required surface finish. In this paper, three MCDM tools, i.e. multi-attributive border approximation area comparison (MABAC), compromise ranking of alternatives from distance to ideal solution (CRADIS) and evaluation based on distance from average solution (EDAS) are adopted in intuitionistic fuzzy (IF) environment to demonstrate their effectiveness in solving parametric optimization problems of a surface grinding process and a cylindrical grinding process, while considering varying opinions of multiple stakeholders with respect to relative importance assigned to the responses. In the surface grinding process, all the three IF-MCDM tools identify grinding wheel speed = 90 m/s, infeed speed = 0.5 m/min and grinding depth = 0.1 mm as the optimal parametric combination. In the cylindrical grinding process, an intermix of different input parameters as work speed = 36 m/min, feed rate = 25 mm/min and depth of cut = 0.02 mm is singled out as the best by IF-MABAC and IF-CRADIS, and ranked second-best by IF-EDAS method. Thus, the derived results validate applicability of the considered IF-MCDM methods in effectively optimizing different grinding processes in an uncertain group decision making environment.